# Text-Independent Speaker Verification with Dual Attention Network

arXiv: https://arxiv.org/abs/2009.05485 · HTML: https://mlsanity.com/paper/2009.05485/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [EfficientTDNN: Efficient Architecture Search for Speaker Recognition](https://mlsanity.com/paper/2103.13581/) (2021)
- [SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification](https://mlsanity.com/paper/2109.08839/) (2021)
- [Parameter-Free Attentive Scoring for Speaker Verification](https://mlsanity.com/paper/2203.05642/) (2022)

## Abstract (arXiv)

This paper presents a novel design of attention model for text-independent speaker verification. The model takes a pair of input utterances and generates an utterance-level embedding to represent speaker-specific characteristics in each utterance. The input utterances are expected to have highly similar embeddings if they are from the same speaker. The proposed attention model consists of a self-attention module and a mutual attention module, which jointly contributes to the generation of the utterance-level embedding. The self-attention weights are computed from the utterance itself while the mutual-attention weights are computed with the involvement of the other utterance in the input pairs. As a result, each utterance is represented by a self-attention weighted embedding and a mutual-attention weighted embedding. The similarity between the embeddings is measured by a cosine distance score and a binary classifier output score. The whole model, named Dual Attention Network, is trained end-to-end on Voxceleb database. The evaluation results on Voxceleb 1 test set show that the Dual Attention Network significantly outperforms the baseline systems. The best result yields an equal error rate of 1:6%.